Inspecting materials for uniformity and detection of anomalies is important in disciplines ranging from manufacturing to science to biology. Inspection often employs microscopy inspection systems to examine and measure specimens. Specimens as used herein refer to an object of examination (e.g., wafer, substrate, etc.) and artifact refers to a specimen, portion of a specimen, features, abnormalities and/or defects in the specimen. For example, artifacts can be electron-based or electronic devices such as transistors, resistors, capacitors, integrated circuits, microchips, etc., biological abnormalities, such as cancer cells, or defects in a bulk material such as cracks, scratches, chips, etc.
Microscopy inspection systems can be used to enhance what a naked eye can see. Specifically, microscopy inspection systems can magnify objects, e.g. features and abnormalities, by increasing the amount of detail that one can see (e.g., optical resolution). Optical resolution, as used herein, refers to the smallest distance between two points on a specimen that can still be distinguished as two separate points that are still perceivable as separate points by a human. Optical resolution can be influenced by the numerical aperture of an objective, among other parameters. Typically, the higher the numerical aperture of an objective, the better the resolution of a specimen which can be obtained with that objective. A single microscopy inspection system can have more than one objective, with each objective having a different resolving power. Higher resolution objectives typically capture more detail than lower resolution objectives. However, higher resolution objectives, e.g. because of their smaller field of view, typically take much longer to scan a specimen than lower resolution objectives.
To obtain higher resolution images, such as those captured according to a higher resolution objective or those created using super-resolution techniques, without sacrificing speed, artificial intelligence models can be used to infer and simulate a super-resolution image from a low-resolution image. Such methods can be achieved without actually scanning the specimen using a higher resolution objective but instead by using all or a portion of a low-resolution image of a specimen, e.g. detected artifacts in a low-resolution image. These methods will be referred to herein interchangeably as super-resolution, super-resolution simulation, super-resolution generation, high-resolution simulation, and the images produced by these methods will be referred to herein interchangeably as super-resolution images and high resolution images that are simulated, e.g. using a high-resolution simulation. Super-resolution images, as used herein, can include images created at resolutions greater than the resolution limits of a microscopy system. Specifically, super-resolution images can include images at resolutions beyond the diffraction limit of a given microscopy system or images created beyond the limits of digital image sensors of a given microscopy system. Super-resolution images, as used herein, can also include images simulated within resolution limits of a given microscopy system, but at a higher resolution than a low resolution image (e.g., a super-resolution image can be an image simulated at the highest resolution at which a microscopy system is capable of imaging).
However, not all artifacts detectable at low resolution are good candidates for generating accurate super-resolution images. For example, an artifact detected using low resolution magnification can correspond to many artifacts detected by high resolution magnification and without additional information, which can be lacking in a low-resolution image of the artifact, it can be impossible to generate an accurate super-resolution image of the low resolution image, e.g. using high resolution simulation.
Accordingly, it is desirable to provide new mechanisms for providing feedback about which artifacts found at low resolution magnification are appropriate or inappropriate for generating super-resolution images. Further, it is desirable to improve the accuracy of generated super-resolution images.